---
title: "HDL-C vs Lipidome Cholesterol"
output:
    flexdashboard::flex_dashboard:
        navbar:
            - { title: "About Fast Food Study", 
                href: "http://18.188.168.203/ffs/hdl/home.Rmd", 
                align: left }
        source_code: embed
        orientation: rows
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = F, warning =F, error = F, message=F)
```

```{r, packages}
pkgs = c('plyr', 'dplyr','stringr','reshape2','tibble', 'plotly', 'DT','data.table',
         'limma','ggthemes','ggplot2','ggsci')
for(pkg in pkgs){
    library(pkg, quietly=TRUE, verbose=FALSE, warn.conflicts=FALSE, 
            character.only=TRUE)
}
```

```{r}
# rm(list=ls())
# setwd('/Users/chenghaozhu/Box Sync/UC Davis/Right Now/Researches/Zivkovic Lab/Fast Food Study/Data/between_assays_analysis/hdl_structure_and_function/')
load("../Rdata/lpd_precalc.Rdata")
load('../../data/hdl_structure_and_function.Rdata')
edata = lipidome$edata
fdata = lipidome$fdata
pdata = lipidome$pdata
```

Row
-------------------------------

### Concentration (mg/dL) vs HDL-C

```{r}
# class concentration in M
# unit: mmol/ml or mol/L (M)
edata_class = edata %>% 
    rownames_to_column("Feature")%>%
    mutate(
        mw = fdata$mol_wt,
        class = fdata$class
        )  %>%
    melt(id.var = c("Feature","mw", "class"),
         variable.name = "Sample",
         value.name = "conc") %>%
    mutate(conc = conc/ (mw * 10^3)) %>%
    ddply(.(class, Sample), summarise,
          conc = sum(conc)) %>%
    dcast(class ~ Sample, value.var = "conc") %>%
    column_to_rownames("class")

lpd_c = as.numeric(edata_class["CE",] + edata_class["Cholesterol",]) * 386.6545 * 1000/10

df = data.frame(
    lpd_c = lpd_c ,
    hdl_c = clinical_data$`HDL-cholesterol (mg/dL)`,
    Subj = pdata$Subj,
    TX = pdata$TX,
    Day = pdata$Day
)

# corr test
corr = cor.test(df$hdl_c, df$lpd_c)

p = ggplot(df, aes(hdl_c, lpd_c)) +
    geom_point(aes(colour = Subj, TX=TX, Day = Day), size=2) +
    stat_smooth(method = "lm") +
    labs(
        x = "HDL-cholesterol (mg/dL)",
        y = "Lipidome Total Chol (mg/dl)",
        title = str_c(
            "HDL-C vs Lipidome Chol\n",
            "P=", format(corr$p.value, scientific = T, digits = 3),
            ", R=", round(corr$estimate, 3))
    ) +
    scale_color_npg() +
    theme_bw()
ggplotly(p)
```

### Concentration (mg/dL) vs Total Chol

```{r}
df = data.frame(
    lpd_c = lpd_c ,
    total_c = clinical_data$`Total Cholesterol (mg/dL)`,
    Subj = pdata$Subj,
    TX = pdata$TX,
    Day = pdata$Day
)

# corr test
corr = cor.test(df$total_c, df$lpd_c)

p = ggplot(df, aes(total_c, lpd_c)) +
    geom_point(aes(colour = Subj, TX=TX, Day = Day), size=2) +
    stat_smooth(method = "lm") +
    labs(
        x = "Total Cholesterol (mg/dL)",
        y = "Lipidome Total Chol (mg/dl)",
        title = str_c(
            "Total Chol vs Lipidome Chol\n",
            "P=", format(corr$p.value,  digits = 3),
            ", R=", round(corr$estimate, 3))
    ) +
    scale_color_npg() +
    theme_bw()
ggplotly(p)
```

Column
-------------------------------

### Proportion vs HDL-C

```{r}
df = data.frame(
    lpd_c = as.numeric(edata_list$class$Proportion["CE",] + edata_list$class$Proportion["Cholesterol",]),
    hdl_c = clinical_data$`HDL-cholesterol (mg/dL)`,
    Subj = pdata$Subj,
    TX = pdata$TX,
    Day = pdata$Day
)

corr = cor.test(df$hdl_c, df$lpd_c)

p = ggplot(df, aes(hdl_c, lpd_c)) +
    geom_point(aes(colour = Subj, TX=TX, Day = Day), size=2) +
    stat_smooth(method = "lm") +
    labs(
        x = "HDL-cholesterol (mg/dL)",
        y = "Lipidome Total Chol (%)",
        title = str_c(
            "HDL-C vs Lipidome Chol (%)\n",
            "P=", format(corr$p.value, digits = 3),
            ", R=", round(corr$estimate, 3))
    ) +
    scale_color_npg() +
    theme_bw()
ggplotly(p)
```

###